Information processing apparatus and presenting method of related items

ABSTRACT

The present invention provides an information processing apparatus including; a first similarity degree calculating section for calculating the degree of similarity between a predetermined item and a calculation target item: and a related item determining section for determining a predetermined quantity of the calculation target items as the related items of the predetermined items in the descending order of the degree of similarity, wherein meta information related to the calculation target item based on the behavior history of user is added to the predetermined items and the degree of similarity between the predetermined item and the calculation target item is calculated.

CROSS REFERENCES TO RELATED APPLICATIONS

The present invention contains subjected matter related to JapanesePatent Application JP 2008-117304 filed in the Japan Patent Office onApr. 28, 2008, the entire contents of which being incorporated hereinfay reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an information processing apparatus anda presenting method of related items.

2. Description of the Related Art

In recent years, a large amount of information has been provided througha network with a rapid progress of information processing technology andcommunication technology. With diversified types of the informationapparatuses possessed by users, diversified kinds of information havebeen presented. For example, some kind of information is distributed toa personal computer (hereinafter referred to as PC) and the like througha wide range network such as Internet and other kind of information suchas broadcasting program and broadcasting information is distributed to aTV receiver and the like. In views of such circumstances, publicattention has been paid to a technology for providing informationpreferred by users (hereinafter referred to as preference information)effectively out of enormous information. For example, high interestshave been paid to a technology for selecting information more conformingto user's preference without any omission.

In relation with this kind of the technology, for example, JapanesePatent Application Laid-Open No. 2005-57713 has described an informationprocessing apparatus for providing more appropriate information to userby taking the preference of individual user into account. Thisinformation processing apparatus has a function for calculating thedegree of correlation between users using data regarding the preferencesof plural users. Further, for example, as regarding two users related toa kind of the degree of correlation, this information processingapparatus has a function for, if a user does not have a factor whichother user has, providing a factor related to that factor to that user.That is, the information which is not presented to the user even if hisor her operation history or the like is used comes to be provided basedon the degree of relation between the user and related other user.

SUMMARY OF THE INVENTION

In addition to the technology disclosed in Japanese Patent ApplicationLaid-Open No. 2005-57713, for example, technologies such ascollaborative filtering (CF) and content based filtering (CBF) areknown. The collaborative filtering method refers to a method forestimating the information which should be automatically presented to acertain user using other user information similar to his or herpreference with preference information regarding a number of usersaccumulated. On the other hand, the content based filtering is a methodfor extracting a content related to a certain content based on thedegree of similarity between the contents. The systems which provideinformation using these methods are sometimes called a recommendationsystem.

As described above, the collaborative filtering is a method forproviding information based on the degree of relation between behaviorhistories of users. That is, this method provides indirectly relatedinformation with respect to a certain content extracted from thebehavior history of user but not providing information directly relatedto a certain content. Further, the collaborative filtering does notextract information conforming to the preference of user unless apredetermined quantity of preference information pieces or more arecollected. Additionally, if the number of information pieces extractedbased on the operation history of user or the like is not sufficient,that number is difficult to increase. On the other hand, the contentbased filtering aims at providing information directly related to acontent and thus, it does not provide the indirectly related informationwith respect, to the content extracted from the behavior history of useror the like.

Accordingly, the present invention has been achieved in views of theabove-described issues and it is desirable to provide a novel andimproved information processing apparatus capable of calculating arelated content by taking information directly related to the contentand information indirectly related to the content into account and apresenting method of related items.

In order to solve the above issue, according to an embodiment of thepresent invention, there is provided an information processing apparatusincluding; a first similarity degree calculating section whichcalculates the degree of similarity between a predetermined item and acalculation target item; and a related item determining section whichdetermines the calculation target items of a predetermined quantity asthe related item of the predetermined item in the descending order ofthe degree of similarity, wherein meta information related to thecalculation target item is added to the predetermined item based onbehavior history of user so as to calculate the degree of similaritybetween the predetermined Item and the calculation target item.

The aforementioned information processing apparatus calculates thedegree of similarity between a predetermined item and a calculationtarget item by means of the first similarity degree calculating section.Further, this information processing apparatus determines apredetermined quantity of the calculation target items to be relateditems of the predetermined item in the descending order of the degree ofsimilarity by means of the related item determining section. Then, thisinformation processing apparatus adds meta information related to thecalculation target item based on the behavior history of user to thepredetermined item and calculates the degree of similarity between thepredetermined item and the calculation target item.

The information processing apparatus may further include a relationdegree adding section which, when the calculation target item iscontained in related items related to the predetermined item, adds thedegree of relation of the calculation target item to the degree ofsimilarity of the calculation target item calculated by the firstsimilarity degree calculating section.

The information processing apparatus may further include: a relationdegree acquiring section which acquires the degree of relation of therelated item related based on the behavior history of user for acandidate item which holds the same meta information as the metainformation of a predetermined item; a similarity degree acquiringsection which acquires the degree of similarity between thepredetermined item and the related item or the candidate item; a secondsimilarity degree calculating section which calculates the similaritydegree between the related item or the candidate item and thepredetermined item based on the degree of relation and the degree ofsimilarity; and a related item adding section which, when the quantityof the related items determined by the related item determining sectionis less than the predetermined quantity, selects items in the descendingorder in terms of the degree of similarity calculated try the secondsimilarity degree calculating section and adds to the related items.

The second similarity degree calculating section may, when a degree ofsimilarity between the predetermined item and the related item isacquired by the similarity degree acquiring section, multiply the degreeof similarity by the degree of relation and regards that multiplicationvalue as a degree of similarity between the predetermined item and thecandidate item. In this case, the related item adding section selectsthe candidate items in the descending order of the degree of similaritycalculated by the second similarity degree calculating section and addsto the related items.

The second similarity degree calculating section may, when the degree ofsimilarity between the predetermined item and the candidate item isacquired by the similarity degree acquiring section, multiply the degreeof similarity by the degree of relation and regards its multiplicationvalue as a degree of similarity between the predetermined item and therelated item. In this case, the related item adding section selects therelated items in the descending order of the degree of similaritycalculated by the second similarity degree calculating section and addsto the related items.

The relation degree acquiring section may be so constructed to acquirethe degree of relation of the related item for the candidate item whichholds meta information having a high degree of priority set for eachmeta information preliminarily of the meta information held by thepredetermined items.

The information processing apparatus may further include a prioritydegree setting section which sets the degree of priority of each metainformation corresponding to the preference of user who accesses thepredetermined items or the statistical data of the meta information heldby the predetermined item. In this case, the relation degree acquiringsection acquires a degree of relation of the related item for thecandidate item which holds meta information having a high degree ofpriority set by the priority degree setting section of the metainformation held by the predetermined item.

The relation degree acquiring section may acquire the degree of relationof the related item for the candidate items which hold all metainformation held by the predetermined item, integrate all the degrees ofrelation for each of the candidate items and regard as the degree ofrelation of the candidate item.

Assuming that an item related to the predetermined item is a firstrelated item and an item related to a N-1-th related item (N≧2) is aN-th related item, the relation degree acquiring section may multiplyall the degrees of relation (2≧K≧N) between the K-1-th related item andthe K-th related item so as to acquire the degree of relation betweenthe first related item and the N-th related item.

The information processing apparatus may further include a related itempresenting section which sorts the related items in the descending orderof the preference of the user so as to present to the user.

In order to solve the above issue, according to another embodiment ofthe present invention, there is provided a related item presentingmethod including the steps of: adding the meta information related to acalculation target item based on the behavior history of user to apredetermined item; calculating the degree of similarity between thepredetermined item and the calculation item; determining a predeterminedquantity of the calculation target, items to be the related item of thepredetermined item in the descending order of the degree of similarity;and presenting the related item to user, the steps being achieved by aninformation processing apparatus.

According to the embodiments of the present invention described above, arelated content which takes information related directly to andinformation related indirectly to a content: into account can becalculated. Particularly, results by the collaborative filtering can becalculated together within the frame of the content based filtering.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an explanatory diagram showing a system configuration of arelated item providing system according to a first embodiment, of thepresent invention;

FIG. 2 is an explanatory diagram showing a configuration example of anoperation log DB according to the embodiment;

FIG. 3 is an explanatory diagram showing a configuration example of anaccess weight DB according to the embodiment;

FIG. 4 is an explanatory diagram showing a configuration example of arelated item meta DB according to the embodiment;

FIG. 5 is an explanatory diagram showing a configuration example of anitem information DB according to the embodiment;

FIG. 6 is an explanatory diagram showing a flow of a related itemproviding method according to the embodiment;

FIG. 7 is an explanatory diagram showing a configuration example of anitem vector and user preference vector according to the embodiment;

FIG. 8 is an explanatory diagram showing a How of the related itemproviding method according to the embodiment;

FIG. 9 is an explanatory diagram showing a calculation method for thedegree of similarity of the related item according to the embodiment;

FIG. 10 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 11 is an explanatory diagram showing a calculation method for thedegree of similarity of the related item according to the embodiment;

FIG. 12 is an explanatory diagram showing a system configuration of arelated item providing system according to a second embodiment of thepresent invention;

FIG. 13 is an explanatory diagram showing a configuration example ofuser preference information DB according to the embodiment;

FIG. 14 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 15 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 16 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 17 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 18 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 19 is an explanatory diagram showing a calculation method for thedegree of similarity of the related item according to the embodiment;

FIG. 20 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 21 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 22 is an explanatory diagram showing a calculation method for thedegree of similarity of the related item according to the embodiment;

FIGS. 23A, 23B and 23C are an explanatory diagram showing a calculationmethod for the degree of similarity of the related item according to theembodiment;

FIG. 24 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIGS. 25A, 25B and 25C are an explanatory diagram showing a calculationmethod for the degree of similarity of the related item according to theembodiment;

FIG. 26 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment;

FIG. 27 is an explanatory diagram showing a flow of the related itemproviding method according to the embodiment; and

FIG. 28 is an explanatory diagram showing a hardware configurationexample of an information processing apparatus capable of achieving thefunction possessed by the related item providing system according toeach of the embodiments of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, preferred embodiments of the present invention wall bedescribed in detail with reference to the appended drawings. Note that,in this specification and the appended drawings, structural elementsthat have substantially the same function and structure are denoted withthe same reference numerals, and repeated explanation of thesestructural elements is omitted.

As a technology for selecting information conforming to the preferenceof user (hereinafter referred to as providing target user) uponproviding the user with information, a technology called collaborativefiltering has been known. According to this technology, for example, asimilar user to the providing target user is searched based on theoperation history of that providing target user or the like and arecommended item which should be provided is calculated from relateditems related to the similar user. Particularly, of the related items ofthe similar user, an item not related to the providing target user isprovided to the providing target user. That is, the recommended item isextracted indirectly through the similarity between the risers.

If picking up a specific example, a processing by the collaborativefiltering is as follows. First, a similar user to the providing targetuser is found out based on the operation history of the providing targetuser. Next, an item which the similar user prefers is extracted andthen, a processing of, if the providing target user does not know theitem preferred by the similar user, recommending that not known item tothe providing target user is executed. The item preferred by the similaruser is the aforementioned related item. As evident from this example,no direct relation between the providing target user and the recommendeditem is considered. In other words, no direct relation between an itemrecommended to the providing target user and an item originallypreferred by the providing target user is considered.

As described above, the relation between the providing target user andthe recommended item is calculated indirectly through a similaritybetween the providing target user and the similar user. Thus, the directrelation between a related item originally related to the providingtarget user and the recommended item is not considered in thecollaborative filtering. In the meantime, the items mentioned hereinclude user himself or herself as well as contents of broadcastingprograms and information relating to the content (for example, programinformation and the like). Therefore, referring to the aforementionedexample, the relation between the providing target user and the similaruser corresponds to a relation between directly related items. Further,the relation between the similar user and the recommended itemcorresponds to the direct relation also. However, the relation betweenthe providing target user and the recommended item can be said to be arelation between indirectly related items via a similar user.

In an initial period just after the providing service of related itemsis started, the frequency of access to an item providing server is oftenlow and further, the number of users is often small. In this case, arecommendation system using the aforementioned collaborative filteringcan hardly provide any related item well conforming to the preference ofuser easily because it is difficult to acquire the preferenceinformation of the user sufficiently (so-called cold start issue).Although, of course, the number of the related items provided increaseswith a passage of time, no sufficient number of the related items may beprovided in the initial period just after the service is started. Thus,technologies for expanding the preference information of user have beenunder a research,

For example, a technology for expanding the preference information ofuser by forecasting meta-.information which user might prefer and usingits forecast result has been proposed. Use of this technology expandsthe number of the preference information pieces to be used in executingthe collaborative filtering, so that a sufficient, number of the relateditems can be provided even in the initial period just after the serviceis started up. That is, this technology is a technology for expandingthe preference information of user based on a forecast.

On the other hand, a technology of calculating the degree of similaritybetween items to recommend an item having a high similarity as a relateditem has been known. This technology is a technology for calculating therelated item by considering only the direct relation between the itemsand called content based filtering. For example, the recommendationsystem using this technology retrieves an item relating to the itempreferred by a providing target user and recommends a found out item tothe providing target, user.

A specific recommendation processing will be described here. Therecommendation system using the content based filtering calculates avector quantity called item vector for each of plural items and thencalculates the degree of similarity between items based on that itemvector. At this time, the recommendation system generates the itemvector based on the meta-information provided to each item. As evidentfrom the aforementioned processing content, the content based filteringcalculates a recommended item based on the degree of direct similaritybetween the items, different from the above-mentioned collaborativefiltering. As for an item whose provided meta-information is small,sometimes it is difficult to calculate any related item related to thatitem.

The embodiment proposed by the inventor of the present inventionprovides a technology for extracting a related item based on the degreeof direct similarity between items like the content based filteringwhile considering the indirect relation between the items like theaforementioned collaborative filtering. In related art, if it isintended to provide a related item by considering both the directsimilarity between the items and the indirect relation between the itemsvia a relation between users, it is necessary to execute a calculationof the collaborative filtering and a calculation of the content basedfiltering separately.

However, use of the technology according to the embodiments mentionedbelow eliminates a necessity for redundant calculations, therebysimplifying arithmetic processing. Consequently, it is expected thatarithmetic operation load applied to the calculation processing of therelated item and its arithmetic operation time are reduced. Further,because the technology according to the embodiments mentioned belowincludes factors of the content, based filtering, it provides a solutionfor the above-described cold start issue.

First Embodiment

First, the first embodiment of the present invention will be described.This embodiment concerns a calculation method for the related item whichenables a result by the collaborative-filtering (CF) to be calculatedtogether within a frame of the content-based-filtering (CBF).Hereinafter, an application method of the related item meta, acalculation method for the related item based on the related item metaand a presentation method of the related item will be described in thisorder. First, a configuration example of a related item providing system100 capable of achieving these methods will be described.

[Configuration Example of Related Item Providing System 100]

Referring to FIG. 1, the system configuration of the related itemproviding system 100 of this embodiment will be described, FIG. 1 is anexplanatory diagram showing a configuration example of the related itemproviding system 100 of this embodiment.

As shown in FIG. 1, the related item providing system 100 includesmainly an operation log DB 102, a preference extracting engine 104, anaccess weight DB 106, a relation extracting engine 108, an related itemmeta DB 110, a recommendation engine 112, an item, information DB 114and a rescue procedure engine 116.

The related item providing system 100 is connected to an informationapparatus 10 operated by user directly or via a network in order toacquire an operation log of the information apparatus 10 and provide arelated item to the information apparatus 10. Although only oneinformation apparatus 10 is represented in FIG. 1 for convenience, twoor more may be provided.

In the meantime, the operation log DB 102, the access weight DB 106, therelated item meta DB 110 and the item information DB 114 are stored in apredetermined memory unit. For example, this memory unit corresponds toa RAM 906, a memory section 920, a removable memory medium 928 or thelike in a hardware configuration example described later.

(Operation Log DB 102)

The operation log DB 102 is a database (DB) which stores the operationlog of the information apparatus 10. That is, information of theoperation log and the like of the information apparatus 10 is acquiredby the related item providing system 100 and stored in the operation logDB 102. Information stored in the operation log DB 102 is read out by apreference extracting engine 104 described later.

Referring to FIG. 2, a configuration example of the operation log DB 102will be described. FIG. 2 is an explanatory diagram (Table 1) showing aconfiguration example of the operation log DB 102 according to thisembodiment. In the example of Table 1, the operation log DB 102 hascolumns about item ID, user ID (member ID), log type and log recordingdate (log time).

The information to be recorded in the column of the item ID isidentification information (ID) indicating each item. The Information tobe recorded in the column of the user ID is identification information(ID) indicating each user. The information to be recorded in the columnof the log type is information indicating the type of each operationlog. The information to be recorded in the column of the log recordingdate is information indicating a date when the operation log of thatrecord is recorded.

If referring to, for example, item ID=1011, 2 is recorded in the userID, “detail” is recorded in the log type and “2007-12-05 08:39:54” isrecorded in the log recording date. That is, the item ID=1011 indicatesthat user with user ID of 2 has made an access at 8 o'clock 39 minutes54 seconds, Dec. 5, 2007, Further, the log type of “detail” indicatesthat the detailed information of that item has been enjoyed. Theconfiguration shown in Table 1 shows only an example and the operationlog of user is recorded together with the time information like thisexample.

(Preference Extracting Engine 104)

Refer to FIG. 1 again. The preference extracting engine 104 acquires anoperation log of user from the operation log DB 102 and calculates anaccess weight to each item for each user. An access weight calculated bythe preference extracting engine 104 is stored in an access weight DB106 described later. In the meantime, the access weight mentioned hereis an index indicating how a certain user prefers a particular item andis information indicating the degree of preference. For example, if thepreference of user with user ID of 2 to an item with item ID of 1011 isstrong, an access weight to user ID of 2 and item ID of 1011 turns to alarge value. That is, the access weight is an example of the preferenceinformation. The calculation method of this access weight will bedescribed later.

(Access Weight DB 106)

The access weight DB 106 is a database in which the access weightcalculated by the preference extracting engine 104 is stored. In otherwords, the access weight DB 106 is a database for storing a result(preference information) obtained by analyzing the operation log ofuser. The information stored in the access weight DB 106 is read out bya relation extracting engine 108 described later.

Here, the configuration example of the access weight DB 106 will bedescribed with reference to FIG. 3. FIG. 3 is an explanatory diagram(Table 2) showing a configuration example of the access weight DB 106according to this embodiment. In the example of Table 2, the accessweight DB 106 has columns about item ID (item ID), user ID (member ID),degree of preference (access weight), initial registration date (entrytime) and update date (update time).

The information to be recorded in the column of the item ID isidentification information (ID) indicating each item. The information tobe recorded in the column of the user ID is identification information(ID) indicating each user. The information to be recorded in the columnof the degree of preference is information indicating the degree ofpreference of each user to each item. That is, the access weightcalculated by the preference extracting engine 104 is recorded in thiscolumn. The information to be recorded in the column of the initialregistration date is time information indicating a date when the initialregistration is made in each record of the access weight DB 106. Theinformation to be recorded in the column of the update date is timeinformation indicating a date when each record of the access weight DB106 is updated.

For example, referring to a record of item ID of 1010, it is evidentthat the degree of preference to an item with an item ID of 1010 of userwith user ID of 2 is 2.4. Further, it is evident that the degree ofpreference of this user has not been updated since the initialregistration at 8 o'clock 40 minutes 52 seconds, Dec. 5, 2007. Further,the degree of preference to an item with an item ID of 1001 and thedegree of preference to an item with an item ID of 1005 are recordedabout user with a user ID of 2 in the access weight DB 106 of Table 2.The preference to an item with an item ID of 1001 is 1.0. On the otherhand, the preference to an item with an item ID of 1005 is 6.0.

Thus, it is evident from the example of Table 2 that user with a userif) of 2 prefers an item with an item ID of 1005 most. Then, it is alsoevident that user with user ID of 2 has a high preference to the itemwith item ID of 1010 and the item with item ID of 1001 in this order.The configuration shown in Table 2 is an example. As the preferenceinformation, user ID, item ID and time information, are recorded in theaccess weight DB 106 like this example, in order to control the degreeof preference of each user.

As described above, the access weight DB 106 stores the user ID, item IDand degree of preference in a relation among them. For the reason, anitem ID with a relatively high degree of preference is extracted and auser ID of user having extracted item IDs in common is extracted so asto calculate a similarity between users. That is, it is considered thatthere is a high similarity recognized between users each having a highdegree of preference to an item with the same item ID. Further, it isalso considered that there exists a certain relation among plural itemsto which the same user has each high degree of preference.

(Relation Extracting Engine 108)

See FIG. 1 again. The relation extracting engine 108 acquiresinformation for calculating an item (related item) related to each itemfrom the access weight. DB 106. This information includes the item ID,user ID and degree of preference. Then, the relation extracting engine108 calculates a related item of each item using the acquiredinformation. For example, the relation extracting engine 108 extractsplural items supplied with the degree of preference of the same user andrelates these items with one another. At this time, one item turns to arelated item of the other item. In the meantime, sometimes pluralrelated items may be extracted.

At this time, the relation extracting engine 108 calculates the degreeof relation indicating the intensity of the relation between a certainitem (an appropriate item) and its related item. For example, therelation extracting engine 108 calculates the degree of the relationbased on the degree of preference of the appropriate item and the degreeof preference of the related item. At this time, the relation extractingengine 108 integrates the degree of preference of the given item and thedegree of preference of the related item and normalizes that, integratedvalue to calculate a degree of relation. The information of the relateditem calculated by the relation extracting engine 108 is recorded in therelated item meta DB 110. The degree of relation is a value calculatedusing a social relation based on user's operation history or workinghistory or activities of community or the like.

(Related Item Meta DB 110)

The related item meta DB 110 is a database which stores information ofthe related item of each item calculated by the relation extractingengine 108 and the degree of the relation between respective items. Theinformation stored in the related item meta DB 110 is read out by arecommendation engine 112 described later.

Here, the configuration example of the related item meta DB 110 will bedescribed with reference to FIG. 4. FIG. 4 is an explanatory diagram(Table 3) showing the configuration example of the related item meta DB110 according to this embodiment. In the example of Table 3, the relateditem meta DB 110 has columns about an item ID (item ID), related item ID(sim item ID), degree of relation (score), initial registration date(entry time) and update date (update time).

The information to be recorded in the column of the item ID isidentification information (ID) indicating each item. The information tobe recorded in the column of the related item ID is identificationinformation (ID) indicating a related item of an item specified by theitem ID. The information to be recorded in the column of the degree ofrelation is a degree of relation indicating the intensity of relationbetween an item specified by the item ID and an item specified by therelated item ID. The information to be recorded in the column of theinitial registration date is time information indicating a date when theinitial registration is made in each record of the related item meta DB110. The information to be recorded in the column of the update date istime information indicating a date when each record of the related itemmeta DB 110 is updated.

If searching for a record corresponding to an item with item ID of 1001from the related item meta DB 110 shown in Table 3 is performed, arecord with related item ID of 1002 and a record with related item ID of1003 are detected. A degree of relation of 1.5 is recorded in the recordwith related item ID of 1002. On the other hand, a degree of relation of3.4 is recorded in the record with related item ID of 1003. From theseinformation pieces, it is evident that, the item with item ID of 1001.is related with two items corresponding to at least item ID of 1002 anditem ID of 1003. Further, it is also evident that the item with item IDof 1001 has a higher relation with the item with item ID of 1003 thanthe item with item ID of 1002.

Thus, item ID, related item ID, and the degree of relation areassociated to each other and recorded in the related item meta DB 110.Consequently, a related item ID supplied with a relatively high degreeof relation is extracted and other item ID of item having the extractedrelated item ID in common is extracted so as to calculate relationbetween the items. That is, it is considered that a high degree ofrelation exists between the items having a high degree of relation withrespect to the item, having the same related item ID.

The degree of relation recorded in the related, item meta DB 110indicates a direct relation between the item with item ID and the itemwith the related item ID. On the other hand, the relation between theitems calculated based on the related item meta DB 110 indicates anindirect relation obtained via the related item.

(Recommendation Engine 112)

See FIG. 1 again. The recommendation engine 112 provides the relateditem of each item to the information apparatus 10 based on informationrecorded in the related item meta DB 110. For example, if a related itemof an item A is requested by the information apparatus 10, therecommendation engine 112 refers to the related item ID of the item Arecorded in the related item meta DB 110 and provides a related itemcorresponding to this related item ID to the information apparatus 10.At this time, the recommendation engine 112 acquires any related itemfrom an item information DB 114 described later. If the quantity ofrelated items provided to the information apparatus 10 is lower than apredetermined quantity, the recommendation engine 112 acquires therelated item from a rescue procedure engine 116 described later.

(Item Information DB 114)

The item information DB 114 is a database for storing attributeinformation applied to each item. The attribute information stored inthe item information DB 114 is read out by the recommendation engine 112and the rescue procedure engine 116 described later.

Here, a configuration example of the item information DB 114 will bedescribed with reference to FIG. 5. FIG. 5 is an explanatory diagramshowing a configuration example (Table 4) of the item information DB 114according to this embodiment. In the example of Table 4, the iteminformation DB 114 has columns about item ID (item ID), meta type(attribute ID), meta (value ID; attribute value), update frequency (noftimes) and importance value (score).

The information to be recorded in the column of the item ID isidentification information (ID) indicating each item. The information tobe recorded in the column of the meta type is information indicating thetype of the attribute information applied to each item. Theidentification information (ID) indicating for example, genre, person,keyword, broadcasting time, broadcasting station, titles and the like isrecorded in this column. For example, the attribute value of each itemis recorded in the column of meta. Identification information (ID)indicating “action”, “YAMADA TARO”, “New Year Day” and the like isrecorded in this column. The information to be recorded in the column ofthe update frequency is the frequency of updating of the record. Theinformation to be recorded in the column of the importance value isinformation indicating the importance value of meta recorded in thatrecord. This importance value is calculated based on the appearancefrequency or the like of that meta when the metas of all the items arereferred to (for example, TF-IDF).

(Rescue Procedure Engine 116)

See FIG. 1 again. The rescue procedure engine 116 calculates a relateditem in a different way from the recommendation engine 112 when itreceives a request about the related item from the recommendation engine112. If the quantity of the related items calculated based on therelated item meta DB 110 or the like is lower than a predeterminedquantity, the recommendation engine 112 requests the rescue procedureengine 116 for the related item. After receiving this request, therescue procedure engine 116 provides a predetermined quantity of therelated items to the recommendation engine 112 so that the quantity ofthe related items reach the predetermined quantity. A calculation methodfor the related item by the rescue procedure engine 116 will bedescribed later.

The functional configuration of the related item providing system 100 ofthis embodiment has been described above. As described above, therelated item providing system 100 calculates the related item of eachitem based on the operation history of user and relation between items.Thus, the related item can be calculated based on the relation betweenthe items taking the preference of user into account. As described inthe description of the access weight DB 106 and the related item meta DB110, a direct, relation between the items and its indirect relation havebeen considered in this calculation.

Unless the quantity of the related items calculated by therecommendation engine 112 reaches a predetermined quantity, thepredetermined quantity of the related items can be provided to theinformation apparatus 10 by replenishing the related item by means ofthe rescue procedure engine 116. In the above description, a detaileddescription of the calculation method of the relation and a calculationmethod of the importance value of the meta applied to each item isomitted. The providing method of the related item as well as thecalculation method will be described in detail below.

[Providing Method of Related Item]

Hereinafter, the related item providing method of this embodiment willbe described in detail. The entire flow of the related item providingmethod of this embodiment will be described with reference to FIG. 6.FIG. 6 is an explanatory diagram showing an entire flow of the relateditem providing method according to this embodiment.

As shown in FIG. 6, first, an analysis range of the operation log storedin the operation log DB 102 is specified by the preference extractingengine 104 (S102). As the analysis range of the operation log, forexample, a startup date and termination date of a log recording date ofthe operation log are specified. Then, a record included in the analysisrange is specified by the preference extracting engine 104.

Next, the preference extracting engine 104 calculates an access weightof each combination of the item ID and user ID based on the operationlog recorded in the record of the analysis range (S104). If there exista plurality of records having the same combination of the item ID anduser ID, the preference extracting engine 104 integrates the accessweights calculated from the operation log recorded in each record andstores that integrated value in the access weight DB 106.

It is permissible to store not the integrated value but a maximum valueof plural access weights in the access weight DB 106. Sometimes anaccess weight to a combination of the item ID and user ID under which anaccess weight is calculated by the preference extracting engine 104 isalready stored in the access weight DB 106. In this case, the preferenceextracting engine 104 integrates a newly calculated access weight and anaccess weight already stored in the access weight DB 106 and overwritesan old stored value with that integrated value. The old stored value maybe overwritten with a larger access weight.

Next, one item is selected from, all items included in the analysisrange of the operation log by the relation extracting engine 108 andprocessings of steps S106 to S122 (or S124) are carried out about thisitem (hereinafter referred to as an appropriate item) (start ofrepetitive processing (A)). First, all users which accessed theappropriate item previously are detected by the relation extractingengine 108 (S106).

Next, one user is selected from all the users which accessed theappropriate item by the relation extracting engine 108, a followingprocessing is carried out for this user (hereinafter referred to as aappropriate user) (start of repetitive processing (B)). First, theaccess weights of all the Items accessed by the appropriate userpreviously are acquired by the relation extracting engine 108 and theaccess weight of each item is integrated (S108). Next, only other useris selected from all the users who accessed the appropriate item andthen the processing of step S108 is executed again (continuation ofrepetitive processing (B)). If the processing of step S108 is executedfor all users who accessed the appropriate item previously, therepetitive processing is terminated (termination of repetitiveprocessing (B)).

After the repetitive processing (B) ends, the integrated value which isintegrated for each item is normalized by the relation extracting engine108 and the normalized integrated value is set in the degree of relationof the appropriate item (S110). Next, the relation extracting engine 108sets up the item whose degree of relation is set up is set up in therelated item of the appropriate item (S112).

After the related item is set up, an item is selected from items havingthe degree of relation with the appropriate item by the relationextracting engine 108 and a following processing is executed for thisitem (hereinafter referred to as the appropriate related item) (startupof repetitive processing (C)). First, whether or not the appropriaterelated item is an item in a validity period is verified by the relationextracting engine 108 (S114). Then, whether or not the appropriaterelated item is valid is determined by the relation extracting engine108 (S116). If the appropriate related item is valid, one of thedifferent appropriate related items is selected and the processings ofsteps S114 to S116 are executed again (continuation of repetitiveprocessing (C)). If the validity period is determined for all therelated items, the procedure proceeds to processing of step S120(termination of repetitive processing (C)).

On the other hand, if the appropriate related item is invalid, theprocedure proceeds to the processing of step S118. In step S118, theinvalid related item is removed from a related item group of theappropriate item by the relation extracting engine 108 (S118). Then, oneof the different appropriate related items is selected and theprocessings of the steps S114 to S116 are executed again (continuationof repetitive processing (C)). However, if the validity period isdetermined for all the related items, the procedure proceeds to theprocessing of step S120 (termination of repetitive processing (C)).

In step S120, the related items are sorted in descending order (inascending order) in terms of the degree of the relation by the relationextracting engine 108 and applied as the related item of the appropriateitem (S120). The application of the related item mentioned here means aregistration of records to the related item meta DB 110. In themeantime, if the quantity of the related items to be registered in therelated item meta DB 110 is lower than a predetermined quantity, all therelated items are registered in the related item meta DB 110 by therelation extracting engine 108.

The recommendation engine 112 determines whether or not the quantity ofthe related items registered in the related Item meta DB 110 has reacheda predetermined quantity (specified quantity) (S122). If the quantity ofthe related items is equal to or smaller than the specified quantity,the procedure proceeds to the processing of step S124 described below(rescue procedure). On the other hand, if the quantity of the relateditems is larger than the specified quantity, one of the differentappropriate items is selected and the processing since step S106 isexecuted again (continuation of repetitive processing (A)). However, ifthe processings of steps S106 to S122 (or S124) are executed for all theitems included in the analysis range of the operation log, a series ofthe processing is terminated (termination of repetitive processing (A)).

(Rescue Procedure)

Here, the rescue procedure of step S124 will be described in detail withreference to FIG. 8. Prior to detailed description of the rescueprocedure, an expression style of item information vector for expressingthe item information of each item simply will be described withreference to FIG. 7. In the meantime, the expression style of the iteminformation vector will be explained here because it is useful forexpressing the preference information of user as described later. FIG. 7is an explanatory diagram showing the expression style of an itemInformation vector and user preference vector according to thisembodiment.

See FIG. 7. As shown in FIG. 7, information of each item is expressed inthe form of a vector. If an item ID is contained in the first element(leftmost column), that vector expresses an item information vector. Onthe other hand, if a user ID is contained in the first element, thatvector expresses the user preference vector. Here, the item informationvector will be described by taking an example.

In the item information vector, for example, each of plural metas ismatched with each meta type and the importance value is applied to eachmeta. In the meantime, the vector indicating the importance value ofeach meta as seen in the meta type unit may be sometimes called metatype vector in a following description. Referring to an example (FIG. 5)of the item information DB 114, evidently, the relation among the itemID, meta type, meta and importance value which constitute the iteminformation vector is described in the item information DB 114. That is,the rescue procedure engine 116 can constitute the item informationvector based on information of the item information DB 114.

Next, the rescue procedure will be described. The processing of thisrescue procedure (step S124) is executed by mainly the rescue procedureengine 116. FIG. 8 is an explanatory diagram showing a flow of rescueprocedure of this embodiment.

First, see FIG. 8. If the rescue procedure is started, as shown in FIG.8, a meta of the meta type 1 applied to the appropriate item is acquiredfrom the item information DB 114 by the rescue procedure engine 116(S130). For example, consider a case where the appropriate item is anitem with item ID of 1005. If the item information DB 114 shown in FIG.5 is presumed, the meta of the meta type 1 applied to the appropriateitem is 153144.

Next, the rescue procedure engine 116 acquires an item group which theitem supplied with the meta of the meta type 1 has as the related item(S132). Referring to the item information DB 114 of FIG. 5, the itemsupplied with the meta (153144) of the meta type 1 is an item with itemID of 1001, 1002, . . . 1014.

In this case, the rescue procedure engine 116 acquires an item havingitem ID of 1001, 1002, . . . 1014 as the related item. For example, ifthe item having the item with item ID of 1003 as the related item isretrieved from the related item meta DB 110 (FIG. 4), the items withitem ID of 1001, 1011 are extracted. Then, the rescue procedure engine116 adds the items with item ID of 1001, 1011 to the item group.Likewise, items having an item with other item ID as the related itemare extracted and added to the item group.

Next, the rescue procedure engine 116 sorts the item group acquired instep S134 in the order of the degree of relation by referring to therelated item meta DB 110 (S134). For example, an item (item ID of 1001,1011) having item ID of 1003 as a related item is considered as an itemcontained in the item group. In this case, the rescue procedure engine116 positions the item with item ID of 1011 having a higher degree ofrelation (degree of relation: 5.4) at a higher level and an item withitem ID of 1001 (degree of relation: 3.4) at a lower level. Other itemscontained in the item group are also sorted in the order of the degreeof relation.

Next, the rescue procedure engine 116 extracts items in the descendingorder of the degree of relation from, the item, group sorted in stepS136 and sets up as a related item of the appropriate item (S136). Incase of the related item meta DB 110 exemplified in FIG. 4, the relateditems set as items with item ID of 1005 are three items with item ID of1001, 1004 and 1010. If a specification number of the related items is5, the rescue procedure engine 116 sets up additional two related items.At this time, the rescue procedure engine 116 extracts item ID of 1003and 1011 having a high degree of relation from the item group (item IDof 1001, 1002, . . . 1011) and sets up as the related item with item IDof 1005.

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item, exceeds aspecified quantity (S138). If the quantity of the related items of theappropriate item is equal to or larger than the specified quantity, therescue procedure engine 116 terminates a series of processing concerningthe rescue procedure. On the other hand, if the quantity of the relateditems of the appropriate item is smaller than the specified quantity,the rescue procedure engine 116 proceeds to the processing of step S140.

In step S140, the rescue procedure engine 116 acquires the meta of themeta type 2 applied to the appropriate item (S140). Next, the rescueprocedure engine 116 acquires an item group having an item supplied withthe meta type 2 as the related item (S142). Next, the rescue procedureengine 116 sorts the item group acquired in step S142 in the order ofthe degree of relation (S144). Next, the rescue procedure engine 116extracts items in the descending order of the degree of relation fromitems included in the item group and sets up in the related item of theappropriate item (S146).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item exceeds aspecified quantity (S148). If the quantity of the related items of theappropriate item is equal to or larger than the specified quantity, therescue procedure engine 116 terminates a series of the processingconcerning the rescue procedure. On the other hand, if the quantity ofthe related items of the appropriate item is smaller than the specifiedquantity, the rescue procedure engine 116 proceeds to the processing ofstep S150.

In step S150, the rescue procedure engine 116 acquires a meta of metatype 3 provided to the appropriate item (S150). Next, the rescueprocedure engine 116 acquires an item group having an item supplied withthe meta type 3 as a related item (S152). Next, the rescue procedureengine 116 sorts the item group acquired in step S152 in the order ofthe degree of relation (S154). The rescue procedure engine 116 extractsitems in the descending order of the degree of relation from Itemsincluded in the item group and sets as the related item of theappropriate item (S156).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item exceeds aspecified quantity (S158). If the quantity of the related items of theappropriate item is equal to or larger than a specified quantity, therescue procedure engine 116 terminates a series of processing concernedwith the rescue procedure. On the other hand, if the quantity of therelated items of the appropriate item is smaller than the specifiedquantity, the rescue procedure engine 116 proceeds to the processing ofstep S160. However, the same processing as steps S150 to S156 is carriedout about meta type 4 to M−1 (M is a natural number) during a proceedingto the processing of step S160.

In step S160, the rescue procedure engine 116 acquires the meta of themeta type M provided to the appropriate item (S160). Next, the rescueprocedure engine 116 acquires an item group having an item supplied withthe meta type M as the related item (S162). Next, the rescue procedureengine 116 sorts the item group acquired in step S162 in the order ofthe degree of relation (S164). Next, the rescue procedure engine 116extracts items in the descending order of the degree of relation fromitems included in the item group and sets up as the related item of theappropriate item (S166).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item exceeds thespecified quantity (S168). If the quantity of the related items of theappropriate item is equal to or larger than the specified quantity, therescue procedure engine 116 terminates a series of processing concerningthe rescue procedure. On the other hand, if the quantity of the relateditems of the appropriate item is smaller than the specified quantity,the rescue procedure engine 116 proceeds to the processing of step S170.In step S170, the access weight DB 106 is referred to by the rescueprocedure engine 116 and an item on a high level of the access weightranking of all users is set up as a related item of the appropriate item(S170). At this time, the rescue procedure engine 116 sets up therelated items so that the quantity of the related items of theappropriate item is of a specified quantity and after that, terminates aseries of processing concerning the rescue procedure.

(Conceptual Explanation)

Here, the conceptual explanation of the above-described rescue procedurewill be made with reference to the conceptual diagram, of FIG. 9. FIG. 9is an explanatory diagram showing the concept of the rescue procedure ofthis embodiment briefly. In this rescue procedure (version for socialrelated usage), the similarity of the item is not calculated using therelation of the meta but the similarity of the item is calculated basedon social knowledge within a community. Thus, the degree of similaritynot expressed in the meta can be considered. This degree of similarityis a value calculated based on the relation of the meta.

A case where no related item exists in the appropriate item as shown inFIG. 9 will be considered. It is assumed that “News” and “Soccer” areprovided to the appropriate item as a meta type. However, theappropriate item is supplied with an ID (attribute ID) of a meta typecorresponding to “News” and “Soccer” actually.

First, any meta of the meta type possessed by the appropriate item isextracted by the rescue procedure engine 116. It is assumed that themeta (attribute value) corresponding to “News” and “Soccer” has beenextracted. Next, the rescue procedure engine 116 acquires an item havingthe extracted meta as a candidate for the related item (hereinafterreferred to as a related item candidate). Assume that an item (1), anitem (2) and an item (3) are acquired as the related item candidates.

These items are supplied with the degree of relation. For example,assume that the item (1) is supplied with a degree of relation 1.5 tothe item A and 0.5 to the item B. Further, assume that the item (2) issupplied with a degree of relation 0.9 to the item B, a degree ofrelation 0.3 to the item C, and a degree of relation 0.1 to the item D.Assume that the item (3) is supplied with a degree of relation 1.0 tothe item A and a degree of relation 0.4 to the item C. Further, assumethat the item B has the same meta (for example, “Soccer”) as the metaextracted from the meta of the appropriate item.

In this case, the rescue procedure engine 116 calculates the degree ofrelation between the appropriate item and each related item candidatebased on the degree of relation (value B) of each related item candidateto the item B and the degree of similarity (value A) between theappropriate item and the item B. For example, this degree of relationcan be calculated based on an expression (1) and an expression (2)mentioned below.

$\begin{matrix}\left\lbrack {{Formula}\mspace{20mu} 1} \right\rbrack & \; \\{{{Degree}\mspace{14mu} {of}\mspace{14mu} {relation}\mspace{14mu} \left( {{{appropriate}\mspace{14mu} {item}},{{item}\mspace{14mu} (1)}} \right)} = {{\left( {{value}\mspace{14mu} A} \right) \times \left( {{value}\mspace{14mu} B} \right)} = {{0.3 \times 0.5} = 0.15}}} & (1) \\{{{Degree}\mspace{14mu} {of}\mspace{14mu} {relation}\mspace{14mu} \left( {{{appropriate}\mspace{14mu} {item}},{{item}\mspace{14mu} (2)}} \right)} = {{\left( {{value}\mspace{14mu} A} \right) \times \left( {{value}\mspace{14mu} B} \right)} = {{0.3 \times 0.9} = 0.27}}} & (2)\end{matrix}$

The degree of relation calculated by the aforementioned expressions (1)and (2) is set to the degree of relation when each related itemcandidate is provided to the appropriate item as the related item. Forexample, the item (1) having the degree of relation 0.15 and the item(2) having the degree of relation 0.27 are provided to the appropriateitem. The related item to be provided to the appropriate item and thedegree of relation to be set in each related item are calculated in thisway. Although the method using an integrated value of the (value A) andthe (value B) has been indicated in the above-described example, it ispermissible to use a method using an additional value of the (value A)and the (value B) or just the (value B) as it is.

in the above description, the method for selecting an item having thesame meta as a meta extracted from the meta group of the appropriateitem has been described as a selection method for the related itemcandidate. This method will be described more in detail.

As the rescue procedure of this embodiment, various selection methodsfor the related item candidate can be applied. For example, a method inwhich the degree of preference is set in each meta in advance and thenan item having a meta with a high degree of preference is selected as arelated item candidate (method 1) can be applied. Further, it ispermissible to apply another method in which the meta group of theappropriate item is expressed with a meta type vector (see FIG. 7) andan item having a high degree of similarity in terms of the meta typevector is selected as the related item candidate (method 2).

Hereinafter, the aforementioned method 2 will be described briefly. Themethod 2 compares the degrees of similarity calculated using the metatype vector between different items. This degree of similarity iscalculated according to an expression (3) below. The calculation methodfor the degree of similarity used here does not need to be of Innerproduct mentioned below but may be any method by calculating a cosinescale or Euclidean distance.

[Formula 2]

Degree of similarity (X, Y)=|X−Y|  (3)

where:

-   X: meta type vector based on the meta group of an appropriate item-   Y: meta type vector based on the meta group of a calculation target    item

According to the aforementioned method 2, the degrees of similaritycalculated by the expression (3) are compared about each calculationtarget item and then, upper N items (N is a predetermined quantity) areselected in the descending order in terms of the degree of similarity asa related item candidate.

MODIFICATION EXAMPLE 1-1

Here, the modification example (modification example 1-1) of the rescueprocedure of this embodiment will be described. According to theabove-described rescue procedure, a related item candidate is extractedbased on the meta of an appropriate item and the related item of theappropriate item is set up based on the degree of relation of therelated item possessed by the related item candidate or the like. Forexample, a method in which an item having a meta similar to the meta ofthe appropriate item is extracted and the related item of that item isset to the related item of the appropriate item can be considered usingthis concept. Then, this method will be described below with referenceto FIG. 10. FIG. 10 is an explanatory diagram showing a flow of therescue procedure according to the modification example 1-1 of thisembodiment.

As shown in FIG. 10, first, the rescue procedure engine 116 retrieves anitem supplied with the meta type 1 (S172). As for the items suppliedwith the meta of the meta type 1, the rescue procedure engine 116extracts upper N items (N is a predetermined quantity) in the descendingorder in terms of the degree of relation from the related itemspossessed by each item. Then, the rescue procedure engine 116 sets upthe extracted item in the related item of the appropriate item (S174).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of an appropriate item exceeds a specifiedquantity (S176). If the quantity of the related items of the appropriateitem is equal to or larger than the specified quantity, the rescueprocedure engine 116 terminates a series of processings concerning therescue procedure. On other hand, if the quantity of the related items ofthe appropriate item is smaller than the specified quantity, the rescueprocedure engine 116 proceeds to the processing of step S178.

In step S178, an item supplied with the meta type 2 is retrieved, by therescue procedure engine 116 (S178). Next, the rescue procedure engine116 extracts upper N items (N is a predetermined quantity) in thedescending order in terms of the degree of relation from the relateditems possessed by each item, for the item supplied with the meta of themeta type 2. Then, the rescue procedure engine 116 sets the extracteditem as the related item of the appropriate item (S180).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item exceeds thespecified quantity (S182). If the quantity of the related items of theappropriate item is equal to or larger than the specified quantity, therescue procedure engine 116 terminates a series of processing concerningthe rescue procedure. On the other hand, if the quantity of the relateditems of the appropriate item is smaller than the specified quantity,the rescue procedure engine 116 proceeds to the processing of step S184.

In step S184, an item supplied with the meta type 3 is retrieved by therescue procedure engine 116 (S184). Next, the rescue procedure engine116 extracts upper N items (N is a predetermined quantity) in thedescending order in terms of the degree of relation from the relateditems possessed by each item for the item, supplied, with the meta ofthe meta type 3. Then, the rescue procedure engine 116 sets up theextracted item as the related item of the appropriate item (S186).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item, exceeds thespecified quantity (S188). If the quantity of the related items of theappropriate item is equal to or larger than the specified quantity, therescue procedure engine 116 terminates a series of processingsconcerning the rescue procedure. On the other hand, if the quantity ofthe related items of the appropriate item is smaller than the specifiedquantity, the rescue procedure engine 116 proceeds to the processing ofstep S190. However, during a proceeding of the processing of step S190,the same processing is carried out for the meta type 4 to M−1 (M is anatural number) as steps S184 to S188.

In step S190, an item supplied with the meta type M is retrieved by therescue procedure engine 116 (S190). Next, the rescuer procedure engine116 extracts upper N items (N is a predetermined quantity) in thedescending order in terms of the degree of relation from the relateditems possessed by each item for the item supplied with the meta of themeta type M. Then, the rescue procedure engine 116 sets up the extractedItem in the related item of the appropriate item (S192).

Next, the rescue procedure engine 116 determines whether or not thequantity of the related items of the appropriate item exceeds thespecified quantity (S194). If the quantity of the related items of theappropriate item is equal to or larger than the specified quantity, therescue procedure engine 116 terminates a series of processingsconcerning the rescue procedure. On the other hand, if the quantity ofthe related items of the appropriate item is smaller than the specifiedquantity, the rescue procedure engine 116 proceeds to the processing ofstep S196. In step S196, the rescue procedure engine 116 refers to theaccess weight DB 106 and sets up items on a high level of the accessweight ranking of all users as the related items of the appropriate item(S196). At this time, the rescue procedure engine 116 sets up therelated items so that, the quantity of the related items of theappropriate item is of the specified quantity and after that, terminatesa series of processings concerning the rescue procedure.

(Conceptual Explanation)

Here, the conceptual description of the rescue procedure of themodification example 1-1 will he made with reference to the conceptualdiagram of FIG. 11. FIG. 11 is an explanatory diagram showing theconcept of the rescue procedure of the modification example 1-1.

Consider a case where no related item, exists in the appropriate item asshown in FIG. 11. Assume that the appropriate item is supplied with“News” and “Soccer” as the meta type. However, the meta type ID(attribute ID) corresponding to the “News” and “Soccer” is provided tothe appropriate item actually.

First, any meta of the meta type of an appropriate item is extracted bythe rescue procedure engine 116. Assume that a meta (attribute value)corresponding to “News” and “Soccer” has been extracted. Next, therescue procedure engine 116 acquires an item having the extracted metaas a candidate for the related item, (hereinafter referred to as relateditem candidate). Then, assume that an item (1), an item (2) and an item(3) are acquired as the related item candidate.

These items are supplied with the degree of relation. For example,assume that the item (1) is supplied with a degree of relation 1.5 to anitem A and a degree of relation 0.5 to an item B. Further, assume thatthe item (2) is supplied with a degree of relation 0.9 to the item B, adegree of relation 0.3 to an item C, and a degree of relation 0.1 to anitem D. Assume that the item (3) is supplied with a degree of relation1.0 to the item A and a degree of relation 0.4 to the item C.

Further, assume that, a degree of similarity (value A) of the relateditem candidate to the appropriate item is calculated by the rescueprocedure engine 116. In the example of FIG. 11, the degree ofsimilarity between the item (1) and the appropriate item is 0.3, thedegree of similarity between the item (2) and the appropriate item is0.0 and the degree of similarity between the item (3) and theappropriate item is 0.0. This example corresponds to a case where thesame meta (for example, “soccer”) as the appropriate item is contained,in the meta of the item (1).

In this case, the rescue procedure engine 116 calculates the degree ofsimilarity between an appropriate item and a related item of eachrelated item candidate based on the degree of similarity (value A)between the appropriate item and each related item candidate and thedegree of relation (value B) of each related item candidate with respectto each related item. This degree of similarity can be calculated basedon, for example, an expression (4) and an expression (5) below. Then, apredetermined quantity of the related items is provided to theappropriate item in the descending order in terms of the degree of thecalculated similarity. In the meantime, because the degree of similaritybetween the item (2)/the item (3) and the related item is 0.0, thedescription of the degree of similarity between these items is omitted.

$\begin{matrix}\left\lbrack {{Formula}\mspace{20mu} 3} \right\rbrack & \; \\{{{Degree}\mspace{14mu} {of}\mspace{14mu} {similarity}\mspace{14mu} \left( {{{appropriate}\mspace{14mu} {item}},{{item}\mspace{14mu} (A)}} \right)} = {{\left( {{value}\mspace{14mu} A} \right) \times \left( {{value}\mspace{14mu} B} \right)} = {{0.3 \times 1.5} = 0.45}}} & (4) \\{{{Degree}\mspace{14mu} {of}\mspace{14mu} {similarity}\mspace{14mu} \left( {{{appropriate}\mspace{14mu} {item}},{{item}\mspace{14mu} (B)}} \right)} = {{\left( {{value}\mspace{14mu} A} \right) \times \left( {{value}\mspace{14mu} B} \right)} = {{0.3 \times 0.5} = 0.15}}} & (5)\end{matrix}$

The degree of similarity calculated by the expressions (4) andexpression (5) is set to the degree of relation when the related item ofeach related item candidate is provided to the appropriate item. Forexample, the item A having the degree of relation 0.45 and the item Bhaving the degree of relation 0.15 are provided to the appropriate item.In this way, the related item to be provided to the appropriate item andthe degree of relation to be set in each related item are calculated. Inthe meantime, although the above-described example indicates a methodusing an integrated value of the (value A) and the (value B), it ispermissible to apply a method using an additional value of the (value A)and the (value B) or just the (value B) as it is.

In the above description, a method for selecting an item having the samemeta as a meta extracted from the meta group of the appropriate item hasbeen described as a selection method for the related item candidate.This method will be described more in detail.

The rescue procedure of this embodiment can adopt various selectionmethods for the related item candidate. For example, it is permissibleto adopt a method in which the degree of preference of each meta is settip preliminarily and an item having a meta with high degree ofpreference is selected as a related item candidate (method 1). Further,it is permissible to adopt another method in which the meta group of anappropriate item is expressed with a meta type vector (see FIG. 7) andan item having a high, degree of similarity in terms of the meta typevector is selected as a related item candidate (method 2).

Hereinafter, the aforementioned method 2 will be described briefly. Themethod 2 compares the degrees of similarity calculated using the metatype vector between items. This degree of similarity is calculatedaccording to an expression (6) below. However, the calculation methodfor the degree of similarity used here does not need to be of innerproduct mentioned below, but may be any method for calculating a cosinescale or Euclidean distance.

[Formula 4]

Degree of similarity (X, Y)=|X·Y|  (6)

where:

-   X: meta type vector based on the meta group of an appropriate item-   Y: meta type vector based on the meta group of a calculation target    item

According to the aforementioned method 2, the degrees of similaritycalculated with the expression (6) are compared about each calculationtarget item and then, upper N items (N is a predetermined quantity) areselected in the descending order in terms of the degree of similarity asthe related item candidate.

Second Embodiments

Next, the second embodiment of the present invention will be described.This embodiment: concerns a related item calculation method whichenables the result by the collaborative filtering (CF) to be calculatedtogether within the frame of the content based filtering (CBF).Particularly, this embodiment concerns an art for calculating therelated item using the preference information of user.

[Configuration Example of Related Item Providing System 200]

First, the system configuration of the related item providing system 200of this embodiment will be described by referring to FIG. 12. FIG. 12 isan explanatory diagram showing a configuration example of the relateditem providing system 200 of this embodiment. In the meantime, likereference numerals are attached to substantially the same functionalconfigurations as the related item providing system 100 of the firstembodiment and thus, a detailed description thereof is not repeatedhere.

As shown in FIG. 12, the related item providing system 200 includesmainly, an operation log DB 102, a preference extracting engine 202, anaccess weight DB 106, a relation extracting engine 108, a related itemmeta DB 110, a recommendation engine 112, an item information DB 114, arescue procedure engine 206 and a user preference information DB 204. Amain difference of this embodiment from the related item providingsystem 100 of the first embodiment exists in the preference extractingengine 202, the function of the rescue procedure engine 206 and anexistence of the user preference information DB 204. Then, thesedifferences will be described.

The related item providing system 200 is connected to an informationapparatus 10 operated by user directly or via a network in order toacquire an operation log of the information apparatus 10 and provide arelated item to the information apparatus 10. Although only oneinformation apparatus 10 is represented in FIG. 12 for convenience, twoor more may be provided.

In the meantime, the operation log DB 102, the access weight DB 106, therelated item meta DB 110, the item information DB 114 and the userpreference information DB 204 are stored in a predetermined memory unit.For example, this memory unit corresponds to a RAM 906, a memory section920 or a removable memory medium 928 or the like in a hardwareconfiguration example described later.

(Preference Extracting Engine 202)

The preference extracting engine 202 acquires a user operation log fromthe operation log DB 102 to calculate an access weight to each item foreach user. The access weight calculated by the preference extractingengine 202 is stored in the access weight DB 106 and the user preferenceinformation DB 204. In the meantime, the access weight mentioned here isan index which indicates how a certain user prefers a particular item orinformation indicating the degree of his or her preference. That is, theaccess weight is an example of the preference information.

For example, a weight value is set up preliminarily for each operationlog and the access weight of each user to each item is calculated basedon that value. Here, an example of the calculation method for the accessweight will be described briefly. First, the preference extractingengine 202 specifies an accessed item based on each operation logacquired from the operation log DB 102. Next, the preference extractingengine 202 acquires the meta of the specified item and the importancevalue (weight value) corresponding to that meta toy referring to theaccess information DB 114. Next, the preference extracting engine 202integrates the importance value of each meta and stores that integratedvalue as the access weight. In the meantime, the access weight may hecalculated for each meta type.

(User Preference Information DB 204)

The user preference information DB 204 is a database which stores theattribute information calculated as the preference information for eachuser. The attribute information stored in the user preferenceinformation DB 204 is read out by the rescue procedure engine 206.

Here, the configuration example of the user preference information DB204 will be described with reference to FIG. 13. FIG. 13 is anexplanatory diagram (Table 5) showing a configuration example of theuser preference information DB 204 of this embodiment. In the example ofTable 5, the user preference information DB 204 has columns about userID (member ID), meta type (attribute ID), meta (value ID; attributevalue), and importance value (score). In the meantime, when each item issupplied with a related item, the meta corresponding to each related,item may be stored.

The information to be recorded in the column of the user ID isidentification information (ID) for identifying each user. Theinformation to be recorded in the column of the meta type is informationindicating the type of the attribute information given to each item. Theidentification information (ID) indicating, for example, genre, person,keyword, broadcasting time, broadcasting station, title and the like isrecorded in this column.

The attribute value of each item is recorded in the column of the meta.For example, the identification information (ID) indicating “Action”,“Yamada Taro” and “New Year” is recorded in this column. The informationto be recorded in the column of the importance value is informationindicating the importance value of a meta recorded in the record. Thisimportance value is calculated based on the appearance frequency of themeta or the like when the metas of all items are referred to (forexample, TF-IDF).

(Rescue Procedure Engine 206)

See FIG. 12 again. The rescue procedure engine 206 calculates a relateditem in a different way from the recommendation engine 112 when itreceives a request about the related item from the recommendation engine112. If the quantity of the related items calculated based on therelated item meta DB 110 or the like is lower than a predeterminedquantity, the recommendation engine 112 requests the rescue procedureengine 116 for the related item. After receiving this request, therescue procedure engine 206 provides a predetermined quantity of therelated items to the recommendation engine 112 so that the quantity ofthe related Items reaches the predetermined quantity. However, therescue procedure engine 206 extracts the related items using the userpreference information different from the rescue procedure engine 116 ofthe first embodiment. Hereinafter, this extraction method will bedescribed in detail.

(Detail of Rescue Procedure)

Here, the rescue procedure of this embodiment will be described withreference to FIG. 14. FIG. 14 is an explanatory diagram showing the flowof the rescue procedure of this embodiment.

The rescue procedure of the first embodiment is carried out based on thedegree of priority of preliminarily set meta types (for example, in theorder of meta type 1, meta type 2, . . . meta type M) and consequently,the related item of an appropriate item is set up. The rescue procedureof this embodiment concerns a method for merging results of processingscarried out for all the meta types. This method enables an item having ahigher degree of relation to be set up in the related item of theappropriate item without being affected by the degree of relation of anyparticular meta type. Hereinafter, this method will be described indetail.

First, the rescue procedure engine 206 acquires all meta types possessedby an appropriate item, selects a meta type (hereinafter referred to asan appropriate meta type; meta type M) and executes processings of stepsS202 to S206 (start of repetitive processing (A)).

In step S202, the meta of the meta type M given to the appropriate itemis acquired by the rescue procedure engine 206 (S202). Next, the rescueprocedure engine 206 acquires items (item group) supplied with the itemhaving the meta of the meta type M as a related item (S204). Next, therescue procedure engine 206 sorts the items in the descending order interms of the degree of relation by referring to the degree of relationwith respect, to each item contained in the item group and records in alist of the related item candidates (S206).

Next, the rescue procedure engine 206 selects other appropriate metatype from the meta types possessed by the appropriate item and executesthe processings of step S202 to S206 again. However, if the processingsof steps S202 to S206 are executed for all the meta types possessed bythe appropriate item, the rescue procedure engine 206 proceeds to theprocessing of step S208.

In step S208, the rescue procedure engine 206 weights the degree ofrelation of each item contained in the related Item candidate listdepending on the degree of priority of each meta type (S208). Althoughthe degree of priority of each meta type may be set up preliminarily, itmay be calculated dynamically. The method for calculating dynamicallywill be described briefly.

As a calculation method for the degree of priority, for example, amethod in which the degree of priority is determined depending on thebias of the meta possessed by an appropriate item (method 1) and amethod in which the degree of priority is determined depending on thebias of preference of user who makes an access (method 2) are available.

(Method 1)

If the method 1 is applied, the rescue procedure engine 206 calculatesthe bias of each meta possessed by an appropriate item with respect toeach meta type and calculates its weight, depending on that bias. Forexample, the rescue procedure engine 206 sets up a large weight value toa meta type having a large bias. By this setting, the relation betweenthe meta type having a large weight value and an item supplied with thatmeta type is regarded as particularly important. As a calculation methodfor the bias, for example, a following method is available. (1) Anaverage of the weight values is calculated as regards the meta containedin each meta type and the higher the average value, the larger biasvalue is set up. (2) A bias value is set up depending on the ratio ofmetas each having a finite value of the metas contained in respectivemeta types.

(Method 2)

When the method 2 is adopted, the rescue procedure engine 206 calculatesthe bias value of preference of user who makes an access like the method1 and calculates its weight value corresponding to that bias value. Forexample, the weight value of a meta type in which the user preference isbiased largely is set large. By this setting, the relation with the metatype which an appropriate user regards as important is regarded asimportant.

After the above-described weighting, the weight values of itemscontained in the related item candidate list in duplicate are summed upby the rescue procedure engine 206 and merged as a new degree ofrelation (S210). That is, if a plurality of records of the same itemexist in the related item candidate list, those records are collected ina single record. At that time, the degree of relation to be recorded inthat record is set in a sum value of the weight values recorded inplural records about the same item.

Next, the rescue procedure engine 206 extracts upper N items (N is apredetermined quantity) in the descending order in terms of the degreeof relation, from items contained in the related item candidate list andsets them as the related item of the appropriate item (S212). Next, therescue procedure engine 206 determines whether or not the quantity ofthe related items reaches a specified quantity (S214). If the quantityof the related items is equal to or larger than the specified quantity,the rescue procedure engine 206 terminates a series of processingsconcerning the rescue procedure. On the other hand, if the quantify ofthe related items is smaller than the specified quantity, the rescueprocedure engine 206 proceeds to the processing of step S216.

In step S216, upper items in the descending order in terms of the accessweight to all users are extracted by the rescue procedure engine 206 andprovided to the related items of the appropriate item (S216). At thistime, items having a higher access weight are added by the rescueprocedure engine 206 until the quantity of the related items of theappropriate item reaches the specified quantity. In this way, therelated items are added to the appropriate item only by the specifiedquantity and a series of the processings concerning the rescue procedureis terminated.

The above-described method enables an item, having a higher degree ofrelation to be calculated as a related item without being affected bythe degree of relation using a particular meta type.

MODIFICATION EXAMPLE 2-1)

Here, the modification example (modification example 2-1) of the rescueprocedure of this embodiment will be described with reference to FIG.15. FIG. 15 is an explanatory diagram showing a flow of the rescueprocedure of the modification example 2-1. This modification example 2-1concerns a calculation method for the degree of priority for determiningthe order of the meta types whose processing is to be executed with apriority. In case of this modification example 2-1, the rescue procedureengine 206 executes the calculation processing for the degree ofpriority at a stage prior to execution of the rescue procedure.

As shown in FIG. 15, first, the rescue procedure engine 206 calculatesthe degree of priority for each meta type (S222). At this time, therescue procedure engine 206 calculates the bias of each meta type forthe meta possessed, by each appropriate item and sets the degrees ofpriority of the meta types in the descending order in terms of the biassize. Next, the rescue procedure engine 206 executes the rescueprocedure in order from a meta type having the highest degree ofpriority (S224).

As described above, a higher degree of priority is set in a meta typehaving a larger meta bias. Then, the rescue procedure engine 206 can setup upper items as the related items of an appropriate item in thedescending order in terms of the degree of priority using this degree ofpriority. Further, the rescue procedure engine 206 can change thequantity of items to be given as the related items corresponding to thebias size calculated for each meta type. According to this method, anitem highly related to the appropriate item is regarded as moreimportant using the meta type having a large bias.

Like the bias of the meta type, the rescue procedure engine 206 maycalculate the bias of the preference of user who makes an access and setthe degree of priority depending on the calculated user preference bias.In this case, the rescue procedure engine 206 selects items in orderfrom an item having a higher user preference bias and sets them as therelated items of an appropriate item. Further, the rescue procedureengine 206 is able to change the quantity of items to be given as therelated items corresponding to the user preference bias. That is,according to this method, an item having a higher user preference biasis regarded as more important as an item highly related to theappropriate item.

MODIFICATION EXAMPLE 2-2)

Next, the modification example (modification example 2-2) of the rescueprocedure of this embodiment will be described with reference to FIGS.16 to 18. FIGS. 16 to 18 are explanatory diagrams showing a How of therescue procedure of the modification example 2-2. The rescue procedureof this modification example 2-2 concerns a method for acquiring relateditems possessed by each related item for each related item of anappropriate item in the descending order in terms of the degree ofrelation so as to give them to the appropriate item.

As shown in FIG. 16, the appropriate item is specified as a seed item bythe rescue procedure engine 206 (S232). Next, the rescue procedureengine 206 acquires a related item group of the seed item (S234). Next,the rescue procedure engine 206 executes the processings of steps S236to S240 for all the items contained in the related item group of theseed item (start of repetitive processing (A)).

Next, the rescue procedure engine 206 acquires an item (hereinafterreferred to as a selection item) from the related item group of the seeditem (S236). Next, the rescue procedure engine 206 acquires a relateditem of the selection item and extracts upper related items in thedescending order in terms of the degree of relation so as to set them asthe related items of the appropriate item (S238). Next, the rescueprocedure engine 206 determines whether or not the quantity of therelated items of the appropriate item reaches a specified, quantity(S240). If the quantity of the related items of the appropriate item isequal to or larger than the specified quantity, the rescue procedureengine 206 terminates a series of the processings of the rescueprocedure.

On the other hand, if the quantity of the related items of theappropriate item is smaller than the specified quantity, the rescueprocedure engine 206 selects a selection item having a next highestdegree of relation from the related items of the seed items and executesthe processings of steps S236 to S240 (continuation of repetitiveprocessing (A)). However, if the processings of steps 8236 to S240 areexecuted on all the related items contained in the related item group ofthe seed item, the procedure proceeds to the repetitive processing (B)(termination of the repetitive processing (A)).

The repetitive processing (B) is executed about related items containedin the related item group of the seed item repeatedly. First, an item isacquired from the related item group of the seed item by the rescueprocedure engine 206 (S242). Next, the rescue procedure engine 206acquires a related item group (item group 1) of the acquired items(S244). Next, the procedure proceeds to the repetitive processing (C).

The repetitive processing (C) is executed for items contained in theitem group 1 repeatedly. The rescue procedure engine 206 acquires anitem from the item group 1 (S246). Next, the rescue procedure engine 206sets upper related items in the descending order in terms of the degreeof relation of the related item group of the acquired items as therelated items of the appropriate item (S248). Next, the rescue procedureengine 206 determines whether or not the quantity of the related itemsof the appropriate item reaches the specified quantity (S250). If thequantity of the related items of the appropriate item is equal to orlarger than the specified quantity, the rescue procedure engine 206terminates a series of processings concerning the rescue procedure.

On the other hand, if the quantity of the related items of theappropriate items is smaller than the specified quantity, the rescueprocedure engine 206 selects other items contained in the item group 1and executes processings of steps S246 to S250 on those items(continuation of repetitive processing (C): FIG. 17). If the processingsof steps S246 to S250 are executed about all the items contained in theitem group 1 (termination of repetitive processing (C): FIG. 17), therescue procedure engine 206 returns to step S242, in which it acquiresother item from the related item group of the seed item and executesprocessings subsequent, to step S244 (continuation of repetitiveprocessing (B)). However, if a series of the processings concerning therepetitive processing (B) is executed about all the items contained inthe related item group of the seed item, the procedure proceeds to therepetitive processing (D) (FIG. 17) (termination of repetitiveprocessing (B)).

See FIG. 17. The repetitive processing (D) is executed for all the itemscontained in the item group 1 repeatedly. The rescue procedure engine206 acquires an item from the item group 1 (S252). Next, the rescueprocedure engine 206 acquires related items (item group 2) of the itemacquired in step S252 (S254). Next, the rescue procedure engine 206proceeds to repetitive processing (E).

The repetitive processing (E) is executed for items contained in theitem group 2 repeatedly. The rescue procedure engine 206 acquires anitem from the item group 2 (S256). Next, the rescue procedure engine 206sets upper related items in the descending order in terms of the degreeof relation of the related item group of the acquired items as therelated items of the appropriate item (S258). Next, the rescue procedureengine 206 determines whether or not the quantity of the related itemsof the appropriate item reaches the specified quantity (S260). If thequantity of the related items of the appropriate item is equal to orlarger than the specified quantity, the rescue procedure engine 206terminates a series of processing of the rescue procedure.

On the other hand, if the quantity of the related items of theappropriate item is smaller than the specified quantity, the rescueprocedure engine 206 selects other item contained in the item group 1and executes the processings of steps S256 to S260 on that item(continuation of repetitive processing (E): FIG. 18). If the rescueprocedure engine 206 executes the processings of steps S256 to S260 onall the items contained in the item group 2 (termination of repetitiveprocessing (D): FIG. 18), the procedure returns to step S252, in whichthe rescue procedure engine 206 acquires other item from the item group1 and executes processings subsequent to step S254 (continuation ofrepetitive processing (D)). However, if a series of the processingsabout the repetitive processing (D) is executed on all the itemscontained in the item group 1, the procedure proceeds to the repetitiveprocessing (F)(FIG. 18) (termination of repetitive processing (D)).

The rescue procedure engine 206 executes the same series of processingsas the repetitive processing (D) on item groups 2 to item group M−1while the procedure proceeds to the repetitive processing (F) after therepetitive processing (D) is terminated. After that, the rescueprocedure engine 206 proceeds to repetitive processing (F).

See FIG. 18. The repetitive processing (F) is executed on all itemscontained in the item group M repeatedly. The rescue procedure engine206 acquires an item from the item group M (S262). Next, the rescueprocedure engine 206 acquires a related item (item group M+1) of theitem acquired in step S262 (S264). Next, the rescue procedure engine 206proceeds to repetitive processing (G).

The repetitive processing (G) is executed on items contained in the itemgroup M+1 repeatedly. The rescue procedure engine 206 acquires an itemfrom the item group M+1 (S266). Next, the rescue procedure engine 206sets upper related items in the descending order in terms of the degreeof relation of the related item group of the acquired items as therelated items of the appropriate item (S268). Next, the rescue procedureengine 206 determines whether or not the quantity of the related itemsof the appropriate item reaches the specified quantity (S270). If thequantity of the related items of the appropriate item is equal to orlarger than the specified quantity, the rescue procedure engine 206terminates a series of processings concerning the rescue procedure.

On the other hand, if the quantity of the related items of theappropriate items is smaller than the specified quantity, the rescueprocedure engine 206 selects other items contained in the item group M+1and executes processings of steps S266 to S270 on those items(continuation of repetitive processing (G)). If the processings of stepsS266 to S270 are executed about all the items contained in the itemgroup M+1 (termination of repetitive processing (G)), the rescueprocedure engine 206 returns to step S262, in which it acquires otheritem from the item group M and executes processings subsequent to stepS264 (continuation of repetitive processing (F)). However, if a seriesof the processings concerning the repetitive processing (F) is executedabout all the items contained in the item group M, the procedureproceeds to the processing of step S272 (termination of repetitiveprocessing CD)).

In step S272, the rescue procedure engine 206 adds Items located onupper level of the access weight ranking of all users to the relateditems of the appropriate item (S272). At this time, the rescue procedureengine 206 executes an addition processing until the quantity of therelated items of the appropriate item reaches a predetermined quantity.After that, the rescue procedure engine 206 terminates a series of theprocessings concerning the rescue procedure.

(Conceptual Explanation)

The concept of the rescue procedure of the modification example 2-2 willbe summarized here with reference to FIG. 19. FIG. 19 is an explanatorydiagram showing the rescue procedure of the modification example 2-2conceptually.

As shown in FIG. 19, the rescue procedure of the modification example2-2 can be expressed conceptually with five steps. Assume that, tworelated items (item 1, item 2) are given to the appropriate item instep 1. Assume that the degree of similarity between the appropriateitem and item 1 is 0.8. Further, assume that, the degree of similaritybetween the appropriate item and item 2 is 0.5.

The rescue procedure engine 206 acquires a related item of theappropriate item here. For example, it is assumed that the item 1 isacquired. Further, assume that two related items (item 3, item 4) aregiven to the item 1 as shown in step 2. Then, assume that, the degree ofsimilarity between the item 1 and item 3 is 0.9 and the degree ofsimilarity between the item 1 and item 4 is 0.3.

Next, the rescue procedure engine 206 sets related items (item 3, item4) of the item 1 as a related item candidate (step 3). Next, the rescueprocedure engine 206 calculates the degree of similarity between theappropriate item and various related item candidate (item 3, item 4)(step 4). As for a calculation method of this degree of similarity, forexample, a method of multiplying the degree of similarity (value A)between the appropriate item and the item 1 by the degree of similarity(value B) between the item 1 and the various related item candidate isavailable like an expression (7) and an expression (8) below.

$\begin{matrix}\left\lbrack {{Formula}\mspace{20mu} 5} \right\rbrack & \; \\{{{Degree}\mspace{14mu} {of}\mspace{14mu} {similarity}\mspace{14mu} \left( {{{appropriate}\mspace{14mu} {item}},{{item}\mspace{14mu} (3)}} \right)} = {{\left( {{value}\mspace{14mu} A} \right) \times \left( {{value}\mspace{14mu} B} \right)} = {{0.8 \times 0.9} = 0.72}}} & (7) \\{{{Degree}\mspace{14mu} {of}\mspace{14mu} {similarity}\mspace{14mu} \left( {{{appropriate}\mspace{14mu} {item}},{{item}\mspace{14mu} (4)}} \right)} = {{\left( {{value}\mspace{14mu} A} \right) \times \left( {{value}\mspace{14mu} B} \right)} = {{0.8 \times 0.3} = 0.24}}} & (8)\end{matrix}$

As described above, a degree of similarity between each related item,candidate and an appropriate item is calculated and the related itemcandidate is added to the related item of the appropriate item (step 5).At this time, the rescue procedure engine 206 gives upper N items (N isa specified quantity) in the descending order in terms of the degree ofsimilarity of the related items as well as the related items provided bythe appropriate item originally.

In the above-described embodiment, as a method for calculating thedegree of relation between the appropriate item and each related itemcandidate, a following modification is possible. For example, assumethat the appropriate item is item 1, the related item candidate is itemN and items of the item group for use in acquiring the related itemcandidate are item 2 to N-1. The degree of relation between the item 1and item N can be expressed as shown in a following expression (9) usingthese items (items 1 to N). However, it is assumed that, the degree ofrelation between the items has been normalized to values between 0 and1.

[Formula 6]

Degree of relation (item 1, item N)−degree of relation (item 1, item2)×degree of relation (item 2, item 3)×degree of relation (item 3, item4)× . . . ×degree of relation (item N-1, item N)   (9)

The processing is started from an actually related item when theaforementioned method is used. Thus, it can he estimated to acquire anitem having a relation to some extent.

[Calculation Method of Related Item]

Here, a method for calculating the related items based on informationstored in the related item meta DB 110 will be described with referenceto FIG. 20. FIG. 20 is an explanatory diagram showing a flow of thecalculation method of the related items of this embodiment. Thecalculation processing of the related item described below is executedmainly by the recommendation engine 112. In the meantime, a relativelyrapid operation can be expected by using this method.

As shown in FIG. 20, a meta given to the related item of a specifieditem is acquired from the related item meta DB 110 by the recommendationengine 112 (S292). Next, upper N metas of the acquired metas are set upin the related items in the descending order in terms of the degree ofrelation by the recommendation engine 112 (S294).

The recommendation engine 112 can simply sort the metas of the relateditems in the order of the degree of relation and set up them in therelated items. However, the recommendation engine 112 can calculate arelated item by combination with information about the degree ofsimilarity or the like using other meta information than the meta of therelated item possessed by each item. For example, the recommendationengine 112 can calculate the related item by combination with a resultby the collaborative filtering and a result by the content basedfiltering. Then, these methods will be described briefly below.

(Calculation Method of Related Item by Calculating the Degree ofSimilarity)

As a method for calculating the degree of similarity between iteminformation vectors, for example, methods by calculating the innerproduct of item information vector, cosine scale, Euclidean distance andthe like are available. The method by using the inner product of theitem information vector will be described here.

Assume that the meta contained in an assembly A of the meta typespossessed by the item is a. Further, assume that the weight of the metaa is Wa. Further, assume that the meta type vector to which the meta aof the appropriate item X belongs is Xa and that the meta type vector towhich the meta a of an item Y which is a calculation target (hereinafterreferred to as target item) is Ya. The degree of similarity between theappropriate item and target, item is calculated according to anexpression (10) below using such, an expression.

[Formula 7]

Degree of similarity (X, Y)=Σ_(aeA)(|Xa·Ya|×Wa)   (10)

In the meantime, although in the above expression (10), a weight Wa ismultiplied to the inner product of two meta type vectors (Xa, Ya), theweight of each meta may be set to be multiplied to each meta typevector.

(Modification Example: Similarity Degree Calculation Method)

Here, the modification example of the similarity degree calculation willbe described with reference to FIG. 21. FIG. 21 is an explanatorydiagram showing a flow of the calculation method for the related itemusing the similarity degree calculation method of this modificationexample. According to this modification example, the degree ofsimilarity is calculated with a vector of each item between theappropriate item and target item group and the degrees of relation ofitems contained in the related item meta possessed by the appropriateitem are integrated. Further, a final degree of similarity is calculatedfrom that integrated value and upper N items are calculated as therelated items in the descending order in terms of the degree ofsimilarity.

As shown in FIG. 21, item information of the appropriate item is readout from the item information DB 114 by the recommendation engine 112(S302). Next, an item group (hereinafter referred to as a target itemgroup) which is a calculation target, is selected by the recommendationengine 112 (S304). Next, the repetitive processing (A) is executed. Thisrepetitive processing (A) is executed for a target item grouprepeatedly. First, the recommendation engine 112 selects a target item(start of repetitive processing (A)) from the target item group so as toacquire the item information of that target item from the iteminformation DB 114 (S306).

Next, the recommendation engine 112 calculates the degree of similaritybetween the appropriate item and the target item based on the iteminformation vector of the acquired item information (S308). At thistime, a calculated degree of similarity is recorded in a result list.After that, other target item is selected by the recommendation engine112 and the processings of steps S306, S308 are executed (continuationof repetitive processing (A)). If the processings of steps S306, S308are executed on all the target items contained in the target item group,the recommendation engine 112 proceeds to the processing of step S310(termination of repetitive processing (A)).

In step S310, an item group contained in the related item meta given tothe appropriate item is acquired by the recommendation engine 112(S310). Next, the recommendation engine 112 proceeds to the repetitiveprocessing (B). This repetitive processing (B) is executed on all theitems contained in the item group of the related item meta repeatedly.First, the recommendation engine 112 selects an item from the acquireditem group (start of repetitive processing (B)) and makes a retrieval tosee whether or not the same item as that item exists in the result list(S312).

Next, the recommendation engine 112 determines whether or not the sameitem exists in the result list (S314). If the same item exists in theresult list, the recommendation engine 112 proceeds to the processing ofstep S316. On the other hand, if no same item exists in the result list,the recommendation engine 112 proceeds to the processing of step S318.

In step S316, the degree of relation set for each related item meta isweighted by the recommendation engine 112 and integrated with the degreeof similarity contained in the result list (S316). On the other hand, instep S318, a value obtained by weighting the degree of relation set ineach related item meta is stored in the result, list as a degree ofsimilarity (S318) by the recommendation engine 112. However, the weightof the related item meta is a value indicating how important the degreeof relation of the related item meta is regarded as and givenpreliminarily.

After that, the recommendation engine 112 selects other item containedin the item group and executes the processings of steps 312 to S316 (orS318) repeatedly (continuation of repetitive processing (B)). If theprocessings of steps S312 to S316 (or S318) are executed on all theitems contained in the item group, the recommendation engine 112proceeds to the processing of step S320.

In step S320, records of the result list are sorted in the descendingorder in terms of the degree of similarity by the recommendation engine112 (S320). Next, the recommendation engine 112 sets upper N items inthe descending order in terms of the degree of similarity as the relateditems of the appropriate item (S322) and terminates a series of theprocessings concerning calculation of the related items.

(Summary of Related Item, Calculation Method by Similarity DegreeCalculation)

The calculation method of the related item by similarity degreecalculation will be described conceptually with reference to FIG. 22.FIG. 22 is an explanatory diagram showing the calculation method of therelated item by the aforementioned similarity degree calculationconceptually.

As shown in FIG. 22, the degree of similarity is calculated based on theitem information vector of an appropriate item and the item informationvector of a target, vector by the recommendation engine 112 (step 1).The degree of similarity calculated here is recorded in the result listfor each target item. Further, the degree of similarity of the relateditem meta of the appropriate item is added to the degree of similarityrecorded in the result list (step 2). At this time, the degree ofsimilarity of each target item with respect to the related item of theappropriate item is multiplied by each predetermined weight. Then, thatmultiplication value is added to the degree of similarity of each targetitem recorded in the result list. After that, records of the resultlist, are sorted in the descending order in terms of the similarity(step 3). The recommendation engine 112 extracts the related items inthe descending order in terms of the degree of similarity from theresult list after sorting and provides them as the related item of theappropriate item.

According to the above-described method, after the calculation of thedegrees of similarity is completed between the item information vectorof an appropriate item and the item information vector of a target item,the weights of the related item metas are summed up. As an applicationof this, for example, it is permissible to add the related item meta asan element of the item information vector when the calculation of thesimilarities is executed between the item information vector of theappropriate item and the item information vector of the target item. Thesame calculation as the above-mentioned method can be achieved usingthis method.

(Application 1)

A method for applying the related item meta to an element of the iteminformation vector will be described briefly with reference to FIGS.23A, 23B, 23C and 24. FIGS. 23A, 23B and 23C are an explanatory diagramshowing a configuration example of the item information vector (or userpreference vector) for use in this method. FIG. 24 is an explanatorydiagram showing a flow of a determination processing of the related itemby the method for applying the related item meta to an element of theitem information vector.

As shown in FIG. 24, first, the recommendation engine 112 acquires iteminformation of an appropriate item from the item information DB 114(S332). Next, the recommendation engine 112 establishes an iteminformation vector based on information of the meta type for use in theappropriate item (S334). Next, the recommendation engine 112 selects atarget vector group which is a calculation target, from the all items(S336). Next, the recommendation engine 112 executes the repetitiveprocessing (A). This repetitive processing (A) is executed on all thetarget, items contained in the target, item group repeatedly.

The recommendation engine 112 selects a target item from the target,item group (start of repetitive processing (A)) and acquires iteminformation of the target item from the item information DB 114 (S338).Next, the recommendation engine 112 establishes an item, informationvector based on information of the meta type for use in the target item(S340). Next, the recommendation engine 112 calculates the degree ofsimilarity between the item information vector of the appropriate itemand the item information vector of the target item and records itscalculation result in the result list (S342). Next, the recommendationengine 112 selects other target, vector contained in the target vectorgroup and executes the processings of steps S338 to S342 for that targetvector again (continuation of repetitive processing (A)). However, ifthe processings of steps S338 to S342 are executed on all the targetvectors contained in the target vector group (termination of repetitiveprocessing (A)), the recommendation engine 112 proceeds to theprocessing of step S344.

In step S344, records of the result list are sorted in the descendingorder of the degree of similarity by the recommendation engine 112(S344). Next, the recommendation engine 112 extracts upper N items inthe descending order of the degree of similarity from the result listand determines them as the related items of the appropriate item (S346).After that, the recommendation engine 112 terminates a series ofprocessings concerning a calculation of the related items based on thesimilarity degree calculation.

Use of this method enables a rapid calculation to be achieved because itcan calculate a result in which elements of the collaborative filteringand the content based filtering are mixed up and further calculation ofthe content based filtering and calculation of the collaborativefiltering are carried out at the same time.

(Application 2)

A method of obtaining a result mixed with the element of thecollaborative filtering by applying the degree of similarity betweenrelated item metas to the result of the content based filtering,different from the above-described method can be considered as anapplication example. In this case, the configuration of the iteminformation vector (or user preference vector) is deformed as shown inFIGS. 25A, 256, 25C. Because the flow of a basic processing issubstantially the same as FIG. 24, a detailed description thereof is notrepeated here.

[Related Item Providing Method]

Next, the providing method of the related item by the recommendationengine 112 will be described with reference to FIGS. 26 and 27. FIG. 26is an explanatory diagram showing a flow of a series of processingsconcerning the providing method of the related item. FIG. 27 is anexplanatory diagram showing a modification example of the processingshown in FIG. 26.

As shown in FIG. 26, the recommendation engine 112 acquires a relateditem of an item, specified by a user via the information apparatus 10(S352). Next, the recommendation engine 112 calculates upper N relateditems in the descending order of the degree of relation of the relateditem metas and provides that related item group to the informationapparatus 10 (S354).

According to the method shown in FIG. 26, the recommendation engine 112calculates the related items without using user preference. Thus, whatpreference an accessing user has is not considered and consequently, thesame item is presented as a related item even if the preference isdifferent. Thus, the processing of FIG. 27 shows a modification of thismethod so as to enable a related item considering the user preference tobe presented. According to the processing of FIG. 27, it is consideredthat the related item meeting the preference of the accessing user canbe presented using the relation between the items. Then, the processingof FIG. 27 will be described.

As shown in FIG. 27, first, the recommendation engine 112 acquires arelated item of an item specified by a user via the informationapparatus 10 (S362). Next, the recommendation engine 112 acquires thepreference of a user who accesses the appropriate item (hereinafterreferred to as an appropriate user) from the user preference informationDB 204 (S364). Next, the recommendation engine 112 calculates the degreeof relation between the preference information of the appropriate userand the related item group acquired in step S362 and sorts the itemscontained in the related item group in the descending order of thedegree of relation (S366). Next, the recommendation engine 112calculates upper N related items in the descending order of the degreeof relation of the related item group and presents the related itemgroup to the information apparatus 10 (S368).

The first and second embodiments of the present invention have beendescribed above. The various modification examples and applicationexamples of each embodiment have been described. By applying the artconcerned with these embodiments, the related item which considers userpreference like the collaborative filtering can be presented within theframe of the content based filtering. Further, even if the quantity ofusers is small, the related item is presented using the degree ofsimilarity between the items, whereby leading to providing a solutionmeans for the cold start issue. If further speaking, the related itemcan be presented to an item never referred to previously. Conversely,the related item can be presented to an item having a small number ofmet as also.

As described above, various effects can be obtained using the art of theabove-described embodiments. Then, this effect is useful for wideningthe range of the preference possessed by user himself or herself.Conversely, recommendation of a related item meeting the latent,preference of user is achieved.

[Hardware Configuration]

The functions of the constituent, elements mentioned above can berealized by an information processing apparatus having, for example, ahardware configuration shown in FIG. 28. FIG. 28 is a diagram forexplaining a hardware configuration of an information processingapparatus which can realize the functions held by the constituentelements of the apparatus.

As shown in FIG. 28, the information processing apparatus mainlyincludes a CPU (Central Processing Unit) 902, a ROM (Read Only Memory)904, a RAM (Random Access Memory) 906, a Host bus 908, a bridge 910, anexternal bus 912, an interface 914, an input unit 916, an output unit918, a storage section 920, a drive 922, a connection port 924, and acommunication unit 926.

The CPU 902 functions as an arithmetic processing unit or a control unitand controls an entire operation of the constituent elements or some ofthe constituent elements on the basis of various programs recorded onthe ROM 904, the RAM 906, the storage section 920, or the removalrecording medium 928. The ROM 904 stores, for example, a program loadedon the CPU 902 or data or the like used in an arithmetic operation. TheRAM 906 temporarily or perpetually stores, for example, a program loadedon the CPU 902 or various parameters or the like arbitrarily changed inexecution of the program. These constituent, elements are connected toeach other by, for example, the host bus 908 which can performhigh-speed data transmission. The host bus 908, for example, isconnected to the external bus 912 in which a data transmission speed isrelatively low through the bridge 910.

The input unit 916 is, for example, an operation unit such as a mouse, akeyboard, a touch panel, button, a switch, or a lever. The input unit916 may be a remote control unit (so-called remote) that can transmit acontrol signal by using an infrared ray or other radio waves. The inputunit 916 includes an input control circuit or the like to transmit,information input by using the operation unit to the CPU 902 through aninput signal.

The output unit 918 is, for example, a display device such as a CRT(Cathode Ray Tube), an LCD (Liquid Crystal Display), a PDF (PlasmaDisplay Panel), or an ELD (Electro-Luminescence Display), an audiooutput device such as a loudspeaker or headphones, a printer, a mobilephone, or a facsimile that, can visually or auditorily notify a user ofacquired information.

The storage section 920 is a device to store various data, and includes,for example, a magnetic storage device such as a hard disk drive (HDD;Hard Disk Drive), a semiconductor storage device, an optical storagedevice, or a magnetooptical storage device, or the like.

The drive 922 is a device that reads information recorded on the removalrecording medium 928 such as a magnetic disk, an optical disk, amagnetooptical disk, or a semiconductor memory or writes information inthe removal recording medium 928. The removal recording medium 928 is,for example, a DVD medium, a Blue-ray medium, an HD-DVD medium, acompact flash (CF; compactFlash) (registered trademark), a memory stick,or an SD memory card (Secure Digital memory card), or the like. As amatter of course, the removal recording medium 928 may be, for example,an IC card (Integrated Circuit Card) on which a non-contact. IC chip ismounted, an electronic device, or the like.

The connection port 924 is a port, such as an USB (Universal Serial Bus)port, an IEEE 1394 port, an SCSI (Small Computer System Interface), anRS-232C port, or an optical audio terminal to which the externalconnection device 930 is connected. The external connection device 930is, for example, a printer, a mobile music player, a digital camera, adigital video camera, an IC recorder, or the like.

The communication unit 926 is a communication device to be connected toa network 932. For example, a communication card for a wired or wirelessLAN (Local Area Network), Bluetooth (registered trademark), or WUSB(Wireless USB), an optical communication router, an ADSL (AsymmetricDigital Subscriber Line) router, various communication modems, or thelike is used. The network 932 connected to the communication unit 926includes a wiredly or wirelessly connected network. For example, theInternet, a home-use LAN, infrared communication, broadcasting,satellite communication, or the like is used.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design and other factors insofar as they are within thescope of the appended claims or the equivalents thereof.

1. An information processing apparatus comprising: a first similaritydegree calculating section which calculates the degree of similaritybetween a predetermined item and a calculation target item; and arelated item determining section which determines the calculationtarget, items of a predetermined quantity as the related item of thepredetermined item in the descending order of the degree of similarity,wherein meta information related to the calculation target item is addedto the predetermined item based on behavior history of user so as tocalculate the degree of similarity between the predetermined item andthe calculation target item.
 2. The information processing apparatusaccording to claim 1, further comprising a relation degree addingsection which, when the calculation target item is contained in relateditems related to the predetermined item, adds the degree of relation ofthe calculation target item to the degree of relation of the calculationtarget item calculated by the first similarity degree calculatingsection.
 3. The information processing apparatus according to claim 1,further comprising: a relation degree acquiring section which acquiresthe degree of relation of the related item related based on the behaviorhistory of user for a candidate item which holds the same metainformation as the meta information of a predetermined item; asimilarity degree acquiring section which acquires the degree ofsimilarity between the predetermined item and the related item or thecandidate item; a second similarity degree calculating section whichcalculates the degree of similarity between the related item or thecandidate item and the predetermined item based on the degree ofrelation and the degree of similarity; and a related item adding sectionwhich, when the quantity of the related items determined by the relateditem determining section is less than the predetermined quantity,selects items in the descending order in terms of the degree ofsimilarity calculated by the second similarity degree calculatingsection and adds to the related items.
 4. The information processingapparatus according to claim 3, wherein the second similarity degreecalculating section, when a degree of similarity between thepredetermined item and the related item is acquired by the similaritydegree acquiring section, multiplies the degree of similarity by thedegree of relation and regards that multiplication value as a degree ofsimilarity between the predetermined item and the candidate item; andthe related item adding section selects the candidate items in thedescending order of the degree of similarity calculated by the secondsimilarity degree calculating section and adds to the related items. 5.The information processing apparatus according to claim 3, wherein thesecond similarity degree calculating section, when the degree ofsimilarity between the pre determined item and the candidate item isacquired by the similarity degree acquiring section, multiplies thedegree of similarity by the degree of relation and regards itsmultiplication value as a degree of similarity between the predetermineditem and the related item; and the related item adding section selectsthe related items in the descending order of the degree of similaritycalculated by the second similarity degree calculating section and addsto the related items.
 6. The information processing apparatus accordingto claim 3, wherein the relation degree acquiring section acquires thedegree of relation of the related item for the candidate item whichholds meta information having a high degree of priority set for eachmeta information preliminarily of the meta information held by thepredetermined items.
 7. The information processing apparatus accordingto claim 3, further comprising a priority degree setting section whichsets the degree of priority of each meta information corresponding tothe preference of user who accesses the predetermined items or thestatistical data of the meta information held by the predetermined item,wherein the relation degree acquiring section acquires a degree ofrelation of the related item for the candidate item which holds metainformation having a high degree of priority set by the priority degreesetting section of the meta information held by the predetermined item.8. The information processing apparatus according to claim 3, whereinthe relation degree acquiring section acquires the degree of relation ofthe related item for the candidate items which hold all meta informationheld by the predetermined item, integrates all the degrees of relationfor each of the candidate items and regards as the degree of relation ofthe candidate item.
 9. The information processing apparatus according toclaim 3, wherein assuming that, an item related to the predetermineditem is a first related item arid an item related to a N-1-th relateditem (N≧2) is a N-th related item, the relation degree acquiring sectionmultiplies all the degrees of relation (2≦K≦N) between the K-1-threlated item, and the K-th related item so as to acquire the degree ofrelation between the first related item and the N-th related item. 10.The information processing apparatus according to claim 1, furthercomprising a related item presenting section which sorts the relateditems in the descending order of the preference of the user so as topresent to the user.
 11. A related item presenting method comprising thesteps of: adding the meta information related to a calculation targetitem based on the behavior history of user to a predetermined item;calculating the degree of similarity between the predetermined item andthe calculation target item; determining a predetermined quantity of thecalculation target items as the related item of the predetermined itemin the descending order of the degree of similarity; and presenting therelated item to a user, the steps being achieved by an informationprocessing apparatus.